论文标题
滚动：在不断变化的环境中临时映射的长期强大的基于LIDAR的本地化
ROLL: Long-Term Robust LiDAR-based Localization With Temporary Mapping in Changing Environments
论文作者
论文摘要
长期场景变化带来了使用预构建的地图对本地化系统面临的挑战。本文提出了一种基于激光雷达的系统，可以针对这些挑战提供强大的本地化。当与预构建图的全局匹配不可靠时，我们的方法始于临时激活映射过程。临时地图将在再次获得可靠匹配后将其合并到预构建的地图上，以进行以后的定位运行。我们进一步整合了LIDAR惯性探射仪（LIO），以提供运动补偿的激光镜扫描，并为全局匹配模块提供可靠的初始姿势猜测。为了生成用于导航目的的平滑实时轨迹，我们通过求解姿势图优化问题来融合探视和全局匹配。我们通过在NCLT数据集上进行的广泛实验来评估我们的本地化系统，包括各种变化的室内和室外环境，结果证明了一年多以来的稳健而准确的定位性能。该实现是在GitHub上开源的。
Long-term scene changes present challenges to localization systems using a pre-built map. This paper presents a LiDAR-based system that can provide robust localization against those challenges. Our method starts with activation of a mapping process temporarily when global matching towards the pre-built map is unreliable. The temporary map will be merged onto the pre-built map for later localization runs once reliable matching is obtained again. We further integrate a LiDAR inertial odometry (LIO) to provide motion-compensated LiDAR scans and a reliable initial pose guess for the global matching module. To generate a smooth real-time trajectory for navigation purposes, we fuse poses from odometry and global matching by solving a pose graph optimization problem. We evaluate our localization system with extensive experiments on the NCLT dataset including a variety of changing indoor and outdoor environments, and the results demonstrate a robust and accurate localization performance for over a year. The implementations are open sourced on GitHub.
